Elevated content of cortisol in hair of patients with severe chronic pain: A novel biomarker for stress
Bibliographic record
Abstract
Hair analysis has been used to reflect long-term systemic exposure to exogenous drugs and toxins. Several studies have demonstrated the feasibility of measuring endogenous steroid hormones, e.g. cortisol, in hair. Recently, a study in macaques showed a significant increase in hair cortisol levels induced by stress. We explored whether hair cortisol levels may be used as a biomarker for long-term stress in humans. Patients with severe chronic pain, aged 18 years or older, receiving opioid treatment for at least one year were recruited. Controls were non-obese (body mass index, BMI < 30 mg/kg(2)) adults. The Perceived Stress Scale (PSS) questionnaire was used to assess perceived stress over the last 4 weeks. A hair sample was obtained from the vertex posterior. Cortisol was measured using an enzyme-linked immunosorbent assay. We included fifteen patients (nine females and six males) and 39 non-obese control subjects (20 females, 19 males). PSS scores (median and range) were significantly higher in chronic pain patients (24: 12-28) than in controls (12: 3-31)(P < 0.001). Hair cortisol contents (median and range) were significantly greater in chronic pain patients (83.1: 33.0-205 g/mg) than in controls (46.1: 27.2-200 pg/mg) (P < 0.01). We conclude that hair cortisol contents are increased in patients with major chronic stress. Measurement of cortisol levels in hair constitutes a novel biomarker of prolonged stress.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".